Conditional distance based matching for one-shot gesture recognition

• We propose a new distance measure called conditional distance between two gestures sequences when we have only one or a few samples per gesture class.
• Conditional distance is the distance between query and model gesture sequences in the presence of a third (anchor) gesture sequence.
• We propose speedup strategy for computing conditional distances by pre-selecting the anchor.
• We also propose a condition distance based simultaneous gesture segmentation and recognition called conditional level building.
• We show results of 82% on a multiple subject dataset spanning 179 classes.

A problem of matching gestures, where there are one or few samples per class, is considered in this paper. The proposed approach shows that much better results are achieved if the distance between the pattern of frame-wise distances of two gesture sequences with a third (anchor) sequence from the modelbase is considered. Such a measure is called as conditional distance and these distance pattern are referred to as “warp vectors”. If these warp vectors are similar, then so are the sequences; if not, they are dissimilar. At the algorithmic core, there are two dynamic time warping processes, one to compute the warp vectors with the anchor sequences and the other to compare these warp vectors. In order to reduce the complexity a speedup strategy is proposed by pre-selecting “good” anchor sequences. Conditional distance is used for individual and sentence level gesture matching. Both single and multiple subject datasets are used. Experiments show improved performance above 82% spanning 179 classes.